CodeTrans model for git commit message generation

Pretrained model on git commit using the t5 base model architecture. It was first released in this repository. This model is trained on tokenized git commit: it works best with tokenized git commit.

Model description

This CodeTrans model is based on the t5-base model. It has its own SentencePiece vocabulary model. It used multi-task training on 13 supervised tasks in the software development domain and 7 unsupervised datasets.

Intended uses & limitations

The model could be used to generate the git commit message for the git commit changes or be fine-tuned on other relevant tasks. It can be used on unparsed and untokenized commit changes. However, if the change is tokenized, the performance should be better.

How to use

Here is how to use this model to generate git commit message using Transformers SummarizationPipeline:

from transformers import AutoTokenizer, AutoModelWithLMHead, SummarizationPipeline

pipeline = SummarizationPipeline(
    model=AutoModelWithLMHead.from_pretrained("SEBIS/code_trans_t5_base_commit_generation_multitask"),
    tokenizer=AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_commit_generation_multitask", skip_special_tokens=True),
    device=0
)

tokenized_code = "new file mode 100644 index 000000000 . . 892fda21b Binary files / dev / null and b / src / plugins / gateway / lib / joscar . jar differ"
pipeline([tokenized_code])

Run this example in colab notebook.

Training data

The supervised training tasks datasets can be downloaded on Link

Training procedure

Multi-task Pretraining

The model was trained on a single TPU Pod V3-8 for 480,000 steps in total, using sequence length 512 (batch size 4096). It has a total of approximately 220M parameters and was trained using the encoder-decoder architecture. The optimizer used is AdaFactor with inverse square root learning rate schedule for pre-training.

Evaluation results

For the git commit message generation task, different models achieves the following results on different programming languages (in BLEU score):

Test results :

Language / Model	Java
CodeTrans-ST-Small	39.61
CodeTrans-ST-Base	38.67
CodeTrans-TF-Small	44.22
CodeTrans-TF-Base	44.17
CodeTrans-TF-Large	44.41
CodeTrans-MT-Small	36.17
CodeTrans-MT-Base	39.25
CodeTrans-MT-Large	41.18
CodeTrans-MT-TF-Small	43.96
CodeTrans-MT-TF-Base	44.19
CodeTrans-MT-TF-Large	44.34
State of the art	32.81

Created by Ahmed Elnaggar | LinkedIn and Wei Ding | LinkedIn